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Characteristics of Spatial–Temporal Differences and Measurement of the Level of Forestry Industry Integration in China

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  • Mingming Jin

    (School of Economics and Management, Beijing Forestry University, No. 35, Tsinghua East Road, Haidian District, Beijing 100083, China)

  • Ni Chen

    (School of Economics and Management, Beijing Forestry University, No. 35, Tsinghua East Road, Haidian District, Beijing 100083, China)

  • Haisheng Sun

    (School of Economics and Management, Beijing Forestry University, No. 35, Tsinghua East Road, Haidian District, Beijing 100083, China)

  • Fangping Cao

    (School of Economics and Management, Beijing Forestry University, No. 35, Tsinghua East Road, Haidian District, Beijing 100083, China)

Abstract

The integration of the forestry industry can effectively resolve the conflict between ecological protection and socioeconomic development while bringing new vitality and growth to traditional forestry. In this study, the level of forestry industry integration in 31 provinces in China from 2005 to 2019 was measured using the Herfindahl index method. With ArcGIS and exploratory spatial data analysis methods, the spatial-temporal distribution characteristics, dynamic change trends, spatial correlation characteristics, and existing problems in China’s forestry industry integration development were analyzed. The results showed that the total output value of forestry integrated products and the output value of each product segment increased, but the proportion of product development was imbalanced, and it was concentrated in the understory planting and collection industry and wood processing and manufacturing industry, leaving substantial room for improvement and integration. The value of the forestry industry integration index also increased overall, but the level of integration was low or moderate. In terms of time, the integration index of most provinces trended upward but failed to break through 0.73, leaving a significant gap between it and deep integration. Spatially, the level of integration of the forestry industry varied across the northeast, central, west, and east, with the central and northeast showing a higher integration degree than the east and west. China’s forestry industry integration showed a significant positive spatial correlation, indicating that spatial factors had become an important factor affecting the development of the forestry industry in various regions. Therefore, it is necessary to strengthen the relevant mechanisms of cross-border cooperation and benefit sharing. Lastly, we identified problems with the integration development of the forestry industry, including insufficient and imbalanced integration, unreasonable structural layout of integration development, and insufficient driving capacity for integration. As a result, there were phased and regional differences in the evolution of forestry industry integration.

Suggested Citation

  • Mingming Jin & Ni Chen & Haisheng Sun & Fangping Cao, 2023. "Characteristics of Spatial–Temporal Differences and Measurement of the Level of Forestry Industry Integration in China," Sustainability, MDPI, vol. 15(11), pages 1-20, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8855-:d:1160355
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    References listed on IDEAS

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    1. Lin Li & Tong Liu & Subo Xu & Zhiwei Tian, 2021. "Evaluation on sustainable development of forest tourism in Heilongjiang Province, China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(9), pages 13382-13402, September.
    2. Shuai Wang & Cunyi Yang & Zhenghui Li, 2022. "Green Total Factor Productivity Growth: Policy-Guided or Market-Driven?," IJERPH, MDPI, vol. 19(17), pages 1-19, August.
    3. Rosenberg, Nathan, 1963. "Technological Change in the Machine Tool Industry, 1840–1910," The Journal of Economic History, Cambridge University Press, vol. 23(4), pages 414-443, December.
    4. Lemola, Tarmo, 2002. "Convergence of national science and technology policies: the case of Finland," Research Policy, Elsevier, vol. 31(8-9), pages 1481-1490, December.
    5. Gambardella, Alfonso & Torrisi, Salvatore, 1998. "Does technological convergence imply convergence in markets? Evidence from the electronics industry," Research Policy, Elsevier, vol. 27(5), pages 445-463, September.
    6. Shuren Wang & Qingyu Chai & Xu Sun & Qiwen Zhang & Hongxian Yu, 2021. "Construction and application of evaluation system for integrated development of agricultural industry in China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(5), pages 7469-7479, May.
    7. Abebe Birara Dessie & Tigabu Dagnew Koye & Abebe Dagnew Koye & Asmamaw Alemu Abitew, 2019. "Analysis of red pepper marketing: evidence from northwest Ethiopia," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 8(1), pages 1-14, December.
    8. Rui Qu & Zaewoong Rhee & Seung-Jong Bae & Sang-Hyun Lee, 2022. "Analysis of Industrial Diversification Level of Economic Development in Rural Areas Using Herfindahl Index and Two-Step Clustering," Sustainability, MDPI, vol. 14(11), pages 1-19, May.
    9. Daniel A. Griffith, 2003. "Spatial Autocorrelation and Spatial Filtering," Advances in Spatial Science, Springer, number 978-3-540-24806-4, Fall.
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